The other chapters are a little more hit and miss, I think. From my layperson’s perspective, one way to think about how to situate this book would be that classical economic theory assumes a rational actor that will always maximize utility, Tversky and Kahneman point that we don’t maximize utility due to our various biases (i.e. we’re often irrational), and Gigerenzer argues that they’re all using a definition of rationality that does not actually apply in a complex, uncertain world where we often need to make decisions under time pressure and with limited information (i.e. seeking utility maximization may not actually be rational in terms of achieving hoped for real world outcomes).
In the 1970s, the term heuristic acquired a different connotation, undergoing a shift from being regarded as a method that makes computers smart to one that explains why people are not smart. This study provides a number of implications for credibility research, educators and library practice.
Where an exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution. The rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make a decision. Bounded rationality is the idea that an individualâ€™s ability to act rationally is constrained by the information they have, the cognitive limitations of their minds, and the finite amount of time and resources they have to make a decision. The examples were meant to show that humans have had plenty of time to develop techniques to analyze situations systematically on behalf of a risk management and long term thinking agenda.
The above studies suggest that similar phenomena may be observed regarding usersâ€™ credibility judgments of Wikipedia. That is, peripheral cues may affect the credibility judgments of college students concerning Wikipedia. Based on dual-process theories, Reinhard and Sporer (2010) conducted a series of experiments to test whether there were relationships between the use of source cues and the levels of task involvement in making credibility judgments. One of their experiments used the attractiveness of images as a source cue, which can be considered as a peripheral cue. They found that only peripheral cues influenced the credibility judgments of participants with low-task involvement, whereas both central and peripheral cues had an impact on the credibility judgments of participants with high-task involvement.
Although variance is likely to be the dominant source of error when observations are sparse, it is nevertheless controllable. This analysis has important implications for the possibility of general-purpose models. To control variance, one must abandon the ideal of general-purpose inductive inference and instead, consider to one degree or another, specialisation (34). Put simply, the bias-variance dilemma shows formally why a mind can be better off with an adaptive toolbox of biased, specialised heuristics. A single, generalpurpose tool with many adjustable parameters is likely to be unstable and incur greater prediction error as a result of high variance.
According to Gerd Gigerenzer, more than 4,500 died as a direct result of the attacks. Besides the almost 3,000 slaughtered in the airliners and their targets on the ground, an additional 1,600 lost their lives in US road accidents during the year that followed because they chose to drive rather than risk flying.
In this article, we summarized a vision of human nature based on an adaptive toolbox of heuristics rather than on traits, attitudes, preferences, and similar internal explanations. We discussed the progress made in developing a science of heuristics, beginning with the discovery of less-is-more effects that contradict the prevailing explanation in terms of accuracy-effort trade-offs. Instead, we argue that the answer to the question â€œWhy heuristics? â€ lies in their ecological rationality, that is, in the environmental structures to which a given heuristic is adapted.
Notice that an unbiased algorithm may suffer from high variance, because the mean function may be precisely the underlying function but the individual functions may suffer from excess variance and hence high error. An algorithmâ€™s susceptibility to bias and variance will always depend on the underlying function and how many observations of this function are available. Our cognitive systems are confronted with the bias-variance dilemma whenever they attempt to make inferences about the world.